In advanced technology nodes, benefits from scaling have become limited in terms of power, performance, and area (PPA), necessitating improvements through design-technology cooptimization (DTCO). While the standard-cell methodology is widely used in modern VLSI design, it inherently constrains the potential of DTCO due to its abstraction at the logic level, limiting optimization at the physical level. To bridge this gap, transistor-level design methodology is desired to break the abstraction of standard cells. Existing research has explored large-scale transistor placement, but a gap remains in developing a routing framework that efficiently addresses transistor-level routing challenges. This paper proposes a pioneering transistorlevel routing framework for large-scale transistor placements, along with an efficient CP-SAT (Constraint ProgrammingSATisfiability) formulation for routing in lower layers. Experimental results demonstrate that the proposed routing framework enables transistor-level designs to achieve significantly reduced total wirelength and high utilization rates for designs with hundreds to thousands of transistors, outperforming traditional standard-cell-based approaches in advanced technology nodes.

The standard-cell methodology is widely adopted in the VLSI design flow due to its scalability, reusability, and compatibility with electronic design automation (EDA) tools. However, the fixed positions and confinement of PMOS and NMOS transistors within its standard cell layout impose limitations on overall wirelength and area optimization. Directly placing individual transistors in a design can provide greater flexibility to explore more diffusion-sharing opportunities, which can potentially result in less wirelength and areas than standard-cell-based designs. Unfortunately, existing transistor placement approaches are limited to a very small scale, e.g., a single standard cell. This paper presents TransPlace, the first transistor-level placement framework that is capable of handling a large number of transistors while considering the overall diffusion sharing and wirelength optimization. Experimental results demonstrate its effectiveness in minimizing wirelength and reducing design area beyond the limits of standard-cell-based designs.

Detailed routing is the most tedious and complex procedure in design automation and has become a determining factor in layout automation in advanced manufacturing nodes. Despite continuing advances in custom integrated circuit (IC) routing research, industrial custom layout flows remain heavily manual due to the high complexity of the custom IC design problem. Besides conventional design objectives such as wirelength minimization, custom detailed routing must also accommodate additional constraints (e.g., path-matching) across the analog/mixed-signal (AMS) and digital domains, making an already challenging procedure even more so. This paper presents a novel detailed routing framework for custom circuits that leverages deep reinforcement learning to optimize routing patterns while considering custom routing constraints and industrial design rules. Comprehensive post-layout analyses based on industrial designs demonstrate the effectiveness of our framework in dealing with the specified constraints and producing sign-off-quality routing solutions.

Recent advances in analog device sizing algorithms show promising results on the automatic schematic design. However, the majority of the sizing algorithms are based on schematic-level simulations and layout-agnostic. The physical layout implementation brings extra parasitics to the analog circuits, leading to discrepancies between schematic and post-layout performance. This performance gap raises questions about the effectiveness of automatic analog device sizing tools. Prior work has leveraged procedural layout generation to account for layout-induced parasitics in the sizing process. However, the need for layout templates makes such methodology limited in application. In this paper, we propose to bridge automatic analog sizing with post-layout performance using state-of-the-art optimization-based analog layout generators. A quantitative study is conducted to measure the impact of layout awareness in state-of-the-art device sizing algorithms. Furthermore, we present our perspectives on the future directions in layout-aware analog circuit schematic design.

Placement is a critical step in layout automation for analog and mixed-signal (AMS)-integrated circuits (ICs). It determines the proximity of devices and influences the wiring topology, significantly impacting post-routing parasitics and coupling capacitance. Existing analog placement techniques mainly focus on geometric constraints in analog building blocks. However, there yet lacks an effective way to consider the system-level signal flow for sensitive AMS circuits. Leveraging prior knowledge from schematics, we propose considering the critical signal paths in automatic AMS placement. A multilevel analog layout automation flow is further developed to reduce manual efforts in synthesizing hierarchical AMS circuits. Experimental results demonstrate the efficiency and effectiveness of our proposed framework with a 22.8% reduction in routed wirelength compared to state-of-the-art AMS placer and a 10-dB improvement in the signal-to-noise-and-distortion ratio (SNDR) for an ADC.

Analog layout synthesis has recently received much attention to mitigate the increasing cost of manual layout efforts. To achieve the desired performance and design specifications, generating layout constraints is critical in fully automated netlist-to-GDSII analog layout flow. However, there is a big gap between automatic constraint extraction and constraint management in analog layout synthesis. This paper introduces the existing constraint types for analog layout synthesis and points out the recent research trends in automating analog constraint extraction. Specifically, the paper reviews the conventional graph heuristic methods such as graph similarity and the recent machine learning approach leveraging graph neural networks. It also discusses challenges and research opportunities.

Generating wells for transistors is an essential challenge in analog circuit layout synthesis. While it is closely related to analog placement, very little research has explicitly considered well generation within the placement process. In this work, we propose a new analytical well-aware analog placer. It uses a generative adversarial network (GAN) for generating wells and guides the placement process. A global placement algorithm spreads the modules given the GAN guidance and optimizes for area and wirelength. Well-aware legalization techniques then legalize the global placement results and produce the final placement solutions. By allowing well sharing between transistors and explicitly considering wells in placement, the proposed framework achieves more than 74% improvement in the area and more than 26% reduction in half-perimeter wirelength over existing placement methodologies in experimental results.

Due to the increasingly complex design rules and geo-metric layout constraints within advanced FinFET nodes, automated placement of full-custom analog/mixed-signal (AMS) designs has become increasingly challenging. Compared with traditional planar nodes, AMS circuit layout is dramatically different for FinFET technologies due to strict design rules and grid-based restrictions for both placement and routing. This limits previous analog placement approaches in effectively handling all of the new constraints while adhering to the new layout style. Additionally, limited work has demonstrated effective routability modeling, which is crucial for successful routing. This paper presents a robust analog placement framework using satisfiability modulo theories (SMT) for efficient constraint handling and routability modeling. Experimental results based on industrial designs show the effectiveness of the proposed framework in optimizing placement metrics while satisfying the specified constraints.

Analog and mixed-signal (AMS) circuit designs still rely on human design expertise. Machine learning has been assisting circuit design automation by replacing human experience with artificial intelligence. This paper presents TAG, a new paradigm of learning the circuit representation from layouts leveraging Text, self Attention and Graph. The embedding network model learns spatial information without manual labeling. We introduce text embedding and a self-attention mechanism to AMS circuit learning. Experimental results demonstrate the ability to predict layout distances between instances with industrial FinFET technology benchmarks. The effectiveness of the circuit representation is verified by showing the transferability to three other learning tasks with limited data in the case studies: layout matching prediction, wirelength estimation, and net parasitic capacitance prediction.

In this paper, we discuss the source of effectiveness of Graph Neural Networks (GNNs) in EDA, particularly in the VLSI design automation domain. We argue that the effectiveness comes from the fact that GNNs implicitly embed the prior knowledge and inductive biases associated with given VLSI tasks, which is one of the three approaches to make a learning algorithm physics-informed. These inductive biases are different to those common used in GNNs designed for other structured data, such as social networks and citation networks. We will illustrate this principle with several recent GNN examples in the VLSI domain, including predictive tasks such as switching activity prediction, timing prediction, parasitics prediction, layout symmetry prediction, as well as optimization tasks such as gate sizing and macro and cell transistor placement. We will also discuss the challenges of applications of GNN and the opportunity of applying self-supervised learning techniques with GNN for VLSI optimization.

Despite continuous efforts in layout automation for full-custom circuits, including analog/mixed-signal (AMS) designs, automated layout tools have not yet been widely adopted in current industrial full-custom design flows due to the high circuit complexity and sensitivity to layout parasitics. Nevertheless, the strict design rules and grid-based restrictions in nanometer-scale FinFET nodes limit the degree of freedom in full-custom layout design and thus reduce the gap between automation tools and human experts. This paper presents AutoCRAFT, an automatic layout generator targeting region-based layouts for advanced FinFET-based full-custom circuits. AutoCRAFT uses specialized place-and-route (P&R) algorithms to handle various design constraints while adhering to typical FinFET layout styles. Verified by comprehensive post-layout analyses, AutoCRAFT has achieved promising preliminary results in generating sign-off quality layouts for industrial benchmarks.

Recent research trends in analog layout synthesis aim for a fully automated netlist-to-GDSII design flow with minimum human efforts. Due to the sensitiveness of analog circuit layouts, symmetry matching between critical building blocks and devices can significantly impact the overall circuit performance. Therefore, providing accurate symmetry constraints for automated layout synthesis tools is crucial to achieving high-quality layouts. This paper presents a novel graph-learning-based framework leveraging unsupervised learning to recognize circuit matching structures by making the most of numerous unlabeled circuits. The proposed framework supports both system-level and device-level symmetry constraints extraction for various large-scale analog/mixed-signal systems. Experimental results show that our framework outperforms state-of-the-art symmetry constraint detection algorithms with remarkable accuracy and runtime improvement.

Despite recent developments in automated analog sizing and analog layout generation, there is doubt whether analog design automation techniques could scale to system-level designs. On the other hand, analog designs are considered major roadblocks for open source hardware with limited available design automation tools. In this work, we present OpenSAR, the first open source automated end-to-end successive approximation register (SAR) analog-to-digital converter (ADC) compiler. OpenSAR only requires system performance specifications as the minimal input and outputs DRC and LVS clean layouts. Compared with prior work, we leverage automated placement and routing to generate analog building blocks, removing the need to design layout templates or libraries. We optimize the redundant non-binary capacitor digital-to-analog converter (CDAC) array design for yield considerations with a template-based layout generator that interleaves capacitor rows and columns to reduce process gradient mismatch. Post layout simulations demonstrate that the generated prototype designs achieve state-of-the-art resolution, speed, and energy efficiency.

Placement is among the most critical steps in analog/mixed-signal (AMS) circuit layout synthesis. It implicitly determines the wiring topology and therefore has considerable impacts on post-layout parasitics and coupling. Existing analog placement techniques are mainly focusing on geometric constraints in analog building blocks. However, there yet lacks an effective way to consider the systemlevel signal flow for sensitive AMS circuits. Leveraging prior knowledge from schematics, we propose to consider the critical signal paths in automatic AMS placement and present an efficient framework. Experimental results demonstrate our proposed framework's efficiency and effectiveness with a 22.8% reduction in routed wire-length compared to state-of-the-art AMS placer and 10 dB improvement in the signal-to-noise-and-distortion ratio (SNDR) for an ADC.

Detailed routing is an intricate and tedious procedure in design automation and has become a crucial step for advanced node enablement. Compared with its advances in digital design, detailed routing for analog/mixed-signal (AMS) integrated circuits (ICs) is still heavily manual. In AMS designs, the sensitive net coupling issues and analog-specific constraints make detailed routing even more challenging. This work presents a novel and efficient detailed routing framework for automated AMS layout synthesis considering industrial design rules as well as analog-specific geometric and electrical constraints. Experimental results demonstrate the efficiency and effectiveness of our approach in optimizing circuit performance while satisfying the specified constraints. Post-layout simulations further prove that our detailed routing results can achieve sign-off quality.